Identifying sound originating from a source of interest can be problematic. This is especially so in the presence of background noise which can be sporadic in nature. Systems which rely on identification of sound originating from a source of interest, such as, for example a voice activity detector, utilize various mechanisms to attempt to distinguish when sound is originating from the source of interest and when sound is merely background noise. These various mechanisms, however, suffer from a number of weaknesses. One such weakness is that many of these various mechanisms are complex in nature and perform resource-intensive computations. As a result, these various mechanisms are generally not suitable for low power or low cost applications. In addition, many of these various mechanisms rely on statistical models or heuristics that are developed through machine learning or template matching which adds to the complexity of these systems. Developing such statistical models or heuristics and the corresponding system components for identifying sound originating from a source of interest usually requires a significant amount of effort.